How distributed control systems are evolving to meet changing future needs
Today’s manufacturing companies operate in a fast-changing world with many intersecting challenges competing for their attention. Facing pressures including the rising costs of energy and raw materials, component shortages, a labor market with changing demographics and the need to compete by meeting consumers’ demands for increasingly customized products, companies are looking to do more with less while still maintaining quality.
The distributed control system (DCS) has been the mainstay of many manufacturing and process industries, ensuring reliable, efficient and safe performance of industrial processes. But can the DCS continue to meet these challenges in an increasingly unpredictable business and technological landscape?
The short answer is yes and it is all down to the “Digital DCS,” which will help companies boost productivity, collaboration and operator engagement, while helping them improve their environmental performance.
Digital means flexible
One of the major challenges in improving DCSs based on existing architectures is to upgrade them without causing major disruption to operations. The DCS is often a mission critical system for industrial users as well as being a hefty investment — replacing them every time a new technology comes along, even one that could have a profoundly beneficial impact on the user’s business, is simply not practical or affordable.
The future DCS will solve this challenge by being more digital, more flexible and more open. The aim is to enable a more modular architecture that gives users the ability to improve the DCS without disrupting the process, effectively separating the core functions of the DCS from those that are less time- or process-critical.
Rather than a top-down approach, users themselves are driving this evolution, working with industry and standards bodies to define their needs and develop new architectures that help the DCS become more flexible and adaptable.
One of these initiatives is NAMUR. Essentially a global consortium of process industry end-user organizations, NAMUR has defined an open architecture model known as NOA (NAMUR Open Architecture).
This model separates the heart of the DCS — the core control and automation functionality — from the other more peripheral functions of monitoring and optimization that are not deemed to be time critical. NOA offers an open and secure environment that allows DCS uses to integrate IT components from the field up to enterprise level.
This gives DCS customers a straightforward way to extend the capabilities of their control systems. These capabilities include the ability to use edge and cloud computing technology. Combining this capability with IIoT enabled sensors allows people and departments across a facility or across the whole company to share a huge amount of data from multiple sources.
A similar approach is being taken by the Open Process Automation Forum (OPAF), an industry consortium formed around end users from multiple and diverse industries including oil and gas, chemical, pulp and paper, and pharma, along with system integrators and automation suppliers such as ABB.
OPAF aims to define a new standards-based, interoperable, intrinsically secure architecture that will set the pattern for future process automation systems.
By using this architecture, DCS users will get easier access to leading-edge capabilities. As well as allowing best-in-class components to be integrated into a DCS, another goal is to ensure that asset owners can continue to use their existing application software, avoiding the need to replace it at additional cost.
The new structure creates a new, separate maintenance and optimization layer where users can more easily access data and analyze it to improve the company’s business.
Effective use of this data is the key to making plantwide improvements. Most installations make use of just a small percentage of the data they collect. By capturing and using more of it, a company can use artificial intelligence (AI), machine learning and edge computing to analyze it and come to conclusions about trends and the future possibility of events.
Acting on these trends in advance, either by using them as the basis of predictive maintenance or using them to plan changes to production facilities, can play a major role in optimizing both plant and process performance. This can include improving operations and quality, maximizing energy efficiency, gaining more production time or cutting waste.
Faster deployment
Openness, interoperability and flexibility can be useful even before the DCS is commissioned. DCS users need to get new DCS solutions online faster than ever before to meet their business goals, while also using fewer resources. Separating the automation hardware and software engineering at the set-up stage is one way of achieving this.
For example, ABB’s Adaptive Execution approach enables multiple teams to carry out project tasks in parallel working from different locations or even in a virtualized cloud engineering environment. If multiple stages of work can be carried out simultaneously, project delivery expenditure can be cut by as much as 40 percent.
Faster deployment of a DCS could come from adopting the more modular approach advocated by the modular automation concept. This aims to move away from the existing method of designing and building monolithic automation systems as large projects. Instead, the modular methodology is based on more flexible modules that can be more easily combined — the result for DCS users is the ability to produce a fully deployed system in the minimum amount of time.
Pre-made and pre-tested automation software modules, with all the elements needed to communicate with both other modules and the overall system, can also save deployment time, while many lower level project execution-related tasks are also ripe for automation. The DCS solutions of the future could eliminate such mundane tasks, enabling engineers and technicians to use their skills more productively to solve problems and helping companies get more from their engineering staff.
Meeting the labor challenge
Although low level processes are already largely run by automation, higher-level decision making is still mainly the preserve of humans. With more widespread use of machine learning and AI techniques, these sophisticated decisions could increasingly be taken by machines.
As well as enabling automation or predictive maintenance, AI and machine learning algorithms will also support engineers in the field. Using mobile devices, technicians can diagnose and solve problems by having access to control systems data showing real-time process conditions.
Augmented Reality (AR) tools can be deployed that allow them to perform repairs with the help of experts or get access to virtual instruction guides and manuals. They can share views of equipment or situations with their colleagues and have digital images superimposed over real views of the equipment.
Opening the way to the future
As user expectations build, the ability of the DCS to adapt to changing requirements will be of paramount importance. Combining the added possibilities of digital technologies with the proven performance of the DCS will offer users more openness, flexibility and automation, opening the way to significant improvements in efficiency, quality, speed and process availability.
